时 间：2026年3月25日（周三）10：00 – 10：45
地 点：普陀校区理科大楼A1714室
报告人：徐安察 浙江工商大学教授
主持人：汤银才 华东师范大学教授
摘 要：
Degradation-based reliability analysis heavily relies on random effects to capture unit-to-unit heterogeneity. However, standard approaches impose a parametric form on these latent variables, incurring significant misspecification risk when the true population exhibits multimodality or skewness. We propose a robust semiparametric Bayesian framework for generalized nonlinear Wiener processes in which the drift-based random effects follow a Dirichlet process mixture, letting the data adaptively determine the complexity of the population structure. We derive analytical approximations to the first-passage-time distribution that retain closed-form expressions for the reliability function and mean-time-to-failure. Posterior inference is performed via an efficient hybrid blocked Gibbs sampler coupled with slice sampling. Extensive simulations show that the method consistently recovers the true latent densities and yields accurate lifetime predictions even under severe distributional misspecification. Two real datasets demonstrate the framework’s ability to uncover latent sub-populations and tighten predictive intervals
报告人简介：
徐安察，浙江工商大学统计学教授，博士生导师。长期从事系统可靠性与管理相关领域的研究，迄今以第一作者或通讯作者在Naval Research Logistics、Scandinavian Journal of Statistics、Journal of Quality Technology、European Journal of Operational Research、IEEE Transactions on Reliability、IISE Transactions等期刊上发表SCI论文60余篇，其中ESI高被引论文6篇。已主持国家自然科学基金以及省部级项目十余项。获浙江省自然科学奖、福建省自然科学奖、第一届全国统计科学技术进步奖等。入选浙江省高校“院士专家结对培养青年英才计划”、浙江省高层次人才特殊支持计划青年人才。目前担任中国运筹学会可靠性分会副理事长、统计期刊《Statistical Theory and Related Fields》Associate Editor。